It is always desired to improve the transmission rate of a wireless communication system in order to provide various broadband information services. Improvement of the transmission rate can be realized by increasing a communication bandwidth, but because there is a limitation in available frequency bands, it is essential to improve spectral efficiency. As a technique for significantly improving the spectral efficiency, a multiple input multiple output (MIMO) technique is attracting attention and has been put into practice in cellular systems, wireless LAN systems, and the like. The amount of improvement of the spectral efficiency realized by the MIMO technique is proportional to the number of transmission and reception antennas. However, the number of reception antennas that can be provided for a terminal apparatus is limited. Therefore, multi-user MIMO (MU-MIMO), in which a plurality of simultaneously connected terminal apparatuses are regarded as a virtual large-scale antenna array and transmission signals from a base station apparatus to the terminal apparatuses are spatially multiplexed, is effective in improving the spectral efficiency.
Because, in MU-MIMO, the transmission signals to the terminal apparatuses are received by the terminal apparatuses as inter-user interference (IUI), the IUI needs to be suppressed. For example, in Long Term Evolution (LTE), which is adopted as one of 3.9th generation mobile wireless communication systems, linear precoding is adopted in which the IUI is suppressed by multiplying, in advance, the transmission signals by a linear filter calculated on the basis of channel information transmitted from the terminal apparatuses. However, because the IUI cannot be effectively suppressed unless the orthogonality of the spatially multiplexed transmission signals of the terminals is high, only a limited amount of improvement can be achieved in MU-MIMO based on the linear precoding.
These days, a MU-MIMO technique that uses nonlinear precoding, in which nonlinear processing is performed by the base station apparatus, is attracting attention. When a modulo operation can be performed by the terminal apparatuses, a perturbation vector including complex numbers (perturbation terms) obtained by multiplying arbitrary Gaussian integers by a certain real number as elements can be added to the transmission signals. Therefore, by appropriately setting the perturbation vector in accordance with a channel state between the base station apparatus and the plurality of terminal apparatuses, required transmission power can be significantly reduced compared to in the linear precoding, in which the perturbation vector is not added, even when the orthogonality of the spatially multiplexed transmission signals of the terminals is not high. In the nonlinear precoding, vector perturbation (VP) described in NPL 1 may be used as a method for realizing optimal transmission performance. However, because the VP is a simultaneous estimation technique in which all selectable perturbation vectors are searched for an optimal perturbation vector, there is a problem in that the amount of operation exponentially increases relative to the number of multiplex terminals.
As a technique for reducing the amount of operation in the VP, SE-VP, which is based on sphere encoding (SE), is discussed in NPL 1. In the VP, in which countless perturbation vectors are added to the transmission signals, there are countless transmission signal candidate points. In the SE-VP, the amount of operation required for the search for a perturbation vector is reduced by performing the search while taking into consideration only transmission signal candidate points existing in a sphere drawn in a multidimensional signal point space. Although the SE-VP can reduce the amount of operation without reducing the transmission performance, an increase in the amount of operation relative to the number of multiplex terminals is still exponential.
In NPL 2, a technique for searching for a perturbation vector in the VP based on an M algorithm using QR decomposition is discussed. This technique will be referred to as QRM-VP hereinafter. The QRM-VP is a sequential search technique, and can suppress an increase in the amount of operation relative to the number of multiplex terminals in a polynomial manner. In the QRM-VP, the amount of operation is reduced by not performing operations on transmission signal candidate points that are irrelevant to the optimal perturbation vector, and the effect of reducing the amount of operation is larger than that of the SE-VP. However, because an operation needs to be performed to determine whether or not each transmission signal candidate point is irrelevant to the optimal perturbation vector, the degree of suppression of the amount of operation achieved while maintaining the transmission performance is limited.
Now, in SU-MIMO, which is MIMO transmission between a base station apparatus and a single terminal apparatus, the transmission performance significantly varies depending on a spatial demultiplexing technique used by the terminal apparatus. Maximum likelihood detection (MLD) is a simultaneous search technique that can realize the best transmission performance but, as with the VP, requires an enormous amount of operation, and although a technique for reducing the amount of operation using a QRM algorithm has been examined as with the VP, there is a limitation in the suppression of the amount of operation as with the QRM-VP. Therefore, in PTL 1, adaptive selection algorithm of surviving symbol replica candidates (ASESS) for reducing the amount of operation in SU-MIMO using the MLD is proposed. The ASESS can decrease the number of candidate signal points whose likelihood is to be detected in the MLD by ranking signal candidate points using simple signal processing. Significant suppression of the amount of operation can also be expected in the QRM-VP by applying an adaptive selection algorithm such as the ASESS, but in reality, a simple technique for decreasing the signal candidate points that is suitable for the QRM-VP has not been disclosed.